Final Project Model Monitoring

try:
    import evidently
except:
    !pip install git+https://github.com/evidentlyai/evidently.git

Collecting git+https://github.com/evidentlyai/evidently.git
  Cloning https://github.com/evidentlyai/evidently.git to /tmp/pip-req-build-xkbl7byo
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  Resolved https://github.com/evidentlyai/evidently.git to commit 725f2679db5ea4e865009577f530702ec0e04fbd
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from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

from evidently import ColumnMapping
from evidently.report import Report
from evidently.metric_preset import DataDriftPreset, ClassificationPreset
from evidently.metrics import ClassificationQualityMetric, DataDriftTable, DatasetDriftMetric

import pandas as pd
import numpy as np

# Define the path to the datasets
train_path = '/content/drive/My Drive/ML Ops/final_model_monitor/train_data.csv'
test_path = '/content/drive/My Drive/ML Ops/final_model_monitor/test_data.csv'
test_adj_path = '/content/drive/My Drive/ML Ops/final_model_monitor/test_data_adj.csv'

# Load the dataset
train = pd.read_csv(train_path)
test = pd.read_csv(test_path)
test_adj = pd.read_csv(test_adj_path)

# Tweak Target Column Name for Similarity and remove unneeded columns
train.rename(columns={'y_train_pred': 'Conversion_Pred'}, inplace=True)
test.rename(columns={'y_test_pred': 'Conversion_Pred'}, inplace=True)
test_adj.rename(columns={'y_test_adj_pred': 'Conversion_Pred'}, inplace=True)

train.drop(columns=['CustomerID','AdvertisingPlatform','AdvertisingTool'], inplace=True)
test.drop(columns=['CustomerID','AdvertisingPlatform','AdvertisingTool'], inplace=True)
test_adj.drop(columns=['CustomerID','AdvertisingPlatform','AdvertisingTool'], inplace=True)

train['Conversion'] = train['Conversion'].astype(int)
train['Conversion_Pred'] = train['Conversion_Pred'].astype(int)

test['Conversion'] = test['Conversion'].astype(int)
test['Conversion_Pred'] = test['Conversion_Pred'].astype(int)

test_adj['Conversion'] = test_adj['Conversion'].astype(int)
test_adj['Conversion_Pred'] = test_adj['Conversion_Pred'].astype(int)

# Define numerical and categorical features
numerical_features = train.select_dtypes(include=['float64', 'int64']).columns.tolist()
categorical_features = train.select_dtypes(include=['object']).columns.tolist()

# Define column mapping
column_mapping = ColumnMapping()
column_mapping.target = 'Conversion'
column_mapping.prediction = 'Conversion_Pred'
column_mapping.numerical_features = numerical_features
column_mapping.categorical_features = categorical_features

# Generate reports
report_original = Report(metrics=[ClassificationQualityMetric(), DataDriftPreset()])
report_original.run(reference_data=train, current_data=test, column_mapping=column_mapping)

report_original.show()